English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Pretrained language models are expected to effectively map input text to aset of vectors while preserving the inherent relationships within the text.Consequently, designing a white-box model to compute metrics that reflect thepresence of specific internal relations in these vectors has become a commonapproach for post-hoc interpretability analysis of pretrained language models.However, achieving interpretability in white-box models and ensuring the rigorof metric computation becomes challenging when the source model lacks inherentinterpretability. Therefore, in this paper, we discuss striking a balance inthis trade-off and propose a novel line to constructing metrics forunderstanding the mechanisms of pretrained language models. We havespecifically designed a family of metrics along this line of investigation, andthe model used to compute these metrics is referred to as the tree topologicalprobe. We conducted measurements on BERT-large by using these metrics. Based onthe experimental results, we propose a speculation regarding the workingmechanism of BERT-like pretrained language models, as well as a strategy forenhancing fine-tuning performance by leveraging the topological probe toimprove specific submodules.

Rethinking the Construction of Effective Metrics for Understanding the Mechanisms of Pretrained Language Models